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Enabling In-Ear Magnetic Sensing: Automatic and User Transparent Magnetometer Calibration

Accepted version
Peer-reviewed

Type

Conference Object

Change log

Authors

Montanari, A 

Abstract

Earables (in-ear wearables) are a new frontier in wearables. Acting both as leisure devices, providing personal audio, as well as sensing platforms, earables could collect sensor data for the upper part of the body, subject to fewer vibrations and random movement variations than the lower parts of the body, due to inherent damping in the musculoskeletal system. These data may enable application domains such as augmented/virtual reality, medical rehabilitation, and health condition screening. Unfortunately, earables have inherent size, shape, and weight constraints limiting the type and position of the sensors on such platforms. For instance, lacking a magnetometer in all earables reference platforms, earables lack reference points. Thus, it becomes harder to work with absolute orientations. Embedding magnetometers in earables is challenging, as these rely heavily on radio (mostly Bluetooth) communication (RF) and contain magnets for magnetic-driven speakers and docking. We explore the feasibility of adding a built-in magnetometer in an earbud, presenting the first comprehensive study of the magnetic interference impacting the magnetometer when placed in an earable: both that caused by the speaker and by RF (music streaming and voice calls) are considered. We find that appropriate calibration of the magnetometer removes the offsets induced by the magnets, the speaker, and the variable interference due to BT. Further, we present an automatic, user-transparent adaptive calibration that obviates the need for alternative, expensive, and error-prone manual, or robotics, calibration procedures. Our evaluation shows how our calibration approach performs under different conditions, achieving convincing results with errors below 3° for the majority of the experiments.

Description

Keywords

46 Information and Computing Sciences, 4608 Human-Centred Computing

Journal Title

2021 IEEE International Conference on Pervasive Computing and Communications, PerCom 2021

Conference Name

2021 IEEE International Conference on Pervasive Computing and Communications (PerCom)

Journal ISSN

2474-2503

Volume Title

Publisher

IEEE

Rights

All rights reserved
Sponsorship
This work is supported by Nokia Bell Labs through their donation to the Centre of Mobile, Wearable Systems, and Augmented Intelligence. AG is supported by the Alan Turing Institute under the EPSRC grant EP/N510129/1 and TU/C/000003.